Automatic annotation of visual deep neural network

doi:10.11772/j.issn.1001-9081.2019101774

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (6): 1593-1600.DOI: 10.11772/j.issn.1001-9081.2019101774

• Artificial intelligence • Previous Articles Next Articles

Automatic annotation of visual deep neural network

LI Ming^1,2, GUO Chenhao^1,2, CHEN Xing^1,2

1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350108, China
2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou Fujian 350108, China

Received:2019-10-19 Revised:2019-12-30 Online:2020-06-10 Published:2020-06-18
Contact: CHEN Xing, born in 1985, Ph. D., associate professor. His research interests include system software, software self-adaptation, cloud computing.
About author:LI Ming, born in 1997, M. S. candidate. His research interests include DNN services composition.GUO Chenhao, born in 1997. His research interests include machine learning, natural language processing.CHEN Xing, born in 1985, Ph. D., associate professor. His research interests include system software, software self-adaptation, cloud computing.
Supported by:
National Key Research and Development Program of China (2018YFB1004800), the Talent Program of Fujian Province for Distinguished Young Scholars in Higher Education, the Guiding Project of Fujian Province (2018H0017).

视觉类深度神经网络的自动标注

李鸣^1,2, 郭晨皓^1,2, 陈星^1,2

1.福州大学数学与计算机科学学院，福州 350108
2.福建省网络计算与智能信息处理重点实验室（福州大学），福州 350108

通讯作者: 陈星(1985—)
作者简介:李鸣(1997—)，男，福建莆田人，硕士研究生，主要研究方向:DNN服务组装。郭晨皓(1997—),男,福建福州人,主要研究方向:机器学习、自然语言处理。陈星(1985—)，男，福建福州人，副教授，博士，主要研究方向：系统软件、软件自适应、云计算。
基金资助:
国家重点研发计划项目（2018YFB1004800）；福建省高校杰出青年科研人才计划项目;福建省引导性项目（2018H0017）。

Abstract

Abstract: Focused on the issue that developers cannot quickly figure out the models they need from various models, an automatic annotation method of visual deep neural network based on natural language processing technology was proposed. Firstly, the field categories of visual neural networks were divided, the keywords and corresponding weights were calculated according to the word frequency and other information. Secondly, a keyword extractor was established to extract keywords from paper abstracts. Finally, the similarities between extracted keywords and the known weights were calculated in order to obtain the application fields of a specific model. With experimental data derived from the papers published in three top international conferences of computer vision: IEEE International Conference on Computer Vision(ICCV), IEEE Conference on Computer Vision and Pattern Recognition(CVPR) and European Conference on Computer Vision(ECCV), the experiments were carried out. The experimental results indicate that the proposed method provides highly accurate classification results with a macro average value of 0.89. The validity of this proposed method is verified.

Key words: computer vision, deep neural network, text classification, keyword extraction, automatic annotation, model application field

摘要： 针对开发人员难以快速从众多模型中找到自己所需的模型的问题，提出了一种基于自然语言处理技术的视觉类深度神经网络的自动标注方法。首先，划分视觉类神经网络的领域类别，根据词频等信息计算关键词及其对应的权值；其次，建立关键词提取器从论文摘要中提取出关键词；最后，将提取得到的关键词和已知权值进行相似度计算，从而得到模型的应用领域。从三大国际计算机视觉领域会议，即国际计算机视觉大会（ICCV）、IEEE国际计算机视觉与模式识别会议（CVPR）和欧洲计算机视觉国际会议(ECCV)发表的论文中选取实验数据进行实验。实验结果表明，所提方法能够提供宏平均值为0.89的高精度分类结果，验证了该方法的有效性。

关键词: 计算机视觉, 深度神经网络, 文本分类, 关键词提取, 自动标注, 模型应用领域

CLC Number:

TP391

LI Ming, GUO Chenhao, CHEN Xing. Automatic annotation of visual deep neural network[J]. Journal of Computer Applications, 2020, 40(6): 1593-1600.

李鸣, 郭晨皓, 陈星. 视觉类深度神经网络的自动标注[J]. 计算机应用, 2020, 40(6): 1593-1600.

References

1 倪晨旭.计算机视觉研究综述[J].电子世界,2018(1):91,93.(NI C X. Computer vision research: a review [J]. Electronics World, 2018(1): 91, 93.)
2 张顺,龚怡宏,王进军.深度卷积神经网络的发展及其在计算机视觉领域的应用[J].计算机学报,2019,42(3):453-482. ZHANGS, GONGY H, WANGJ J. The development of deep convolution neural network and its applications on computer vision its applications on computer vision [J]. Chinese Journal of Computers, 2019, 42(3): 453-482.
3 MAHATAD, KURIAKOSEJ, SHAHR R, et al. Key2Vec: automatic ranked keyphrase extraction from scientific articles using phrase embeddings [C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2018: 634-639.
4 战学刚,吴强.基于TF统计和语法分析的关键词提取算法[J].计算机应用与软件,2014,31(1):47-49,92. ZHANX G, WUQ. Keyword extraction algorithm based on TF statistics and syntactic parsing [J]. Computer Applications and Software, 2014, 31(1):47-49, 92.
5 NGUYENT D, KANM Y. Keyphrase extraction in scientific publications [C]// Proceedings of the 2007 International Conference on Asian Digital Libraries, LNCS 4822. Berlin: Springer, 2007: 317-326.
6 HACOHEN-KERNERY. Automatic extraction of keywords from abstracts [C]// Proceedings of the 2003 International Conference on Knowledge-Based Intelligent Information and Engineering Systems, LNCS 2773. Berlin: Springer, 2003: 843-849.
7 姜霖,王东波.采用连续词袋模型(CBOW)的领域术语自动抽取研究[J].现代图书情报技术,2016(2):9-15. JIANGL, WANGD B. Automatic extraction of domain terms using continuous bag-of-words model [J]. New Technology of Library and Information Service, 2016(2): 9-15.
8 余小军,刘峰,张春.基于N-Gram文本特征提取的改进算法[J].现代计算机(专业版),2012(34):3-7. YUX J, LIUF, ZHANGC. Improved text feature extraction algorithm based on N-Gram [J]. Modern Computer, 2012(34): 3-7.
9 罗燕,赵书良,李晓超,等.基于词频统计的文本关键词提取方法[J].计算机应用,2016,36(3):718-725. LUOY, ZHAOS L, LIX C, et al. Text keyword extraction method based on word frequency statistics [J]. Journal of Computer Applications, 2016, 36(3): 718-725.
10 LIT, BAIJ, YANGX, et al. Co-occurrence network of high-frequency words in the bioinformatics literature: structural characteristics and evolution [J]. Applied Sciences, 2018, 8(10): Article No.1994.
11 柳佳刚,陈山.基于PAT-tree的中文关键词自动检索模式的研究[J].计算技术与自动化,2009,28(2):119-123. LIUJ G, CHENS. Pattern research of automatic Chinese keyword retrieval based on PAT-tree [J]. Computing Technology and Automation, 2009, 28(2): 119-123.
12 ZHAOL, HUANGL, ZANGL, et al. LMLSTM: extract event-oriented keyphrase from news stream [C]// Proceedings of the 2019 International Joint Conference on Neural Networks. Piscataway: IEEE, 2019: 1-7.
13 LIUZ, HUANGW, ZHENGY, et al. Automatic keyphrase extraction via topic decomposition [C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2010:366-376.
14 HASANK S, NG V. Conundrums in unsupervised keyphrase extraction: making sense of the state-of-the-art [C]// Proceedings of the 23rd International Conference on Computational Linguistics. Stroudsburg: ACL, 2010:365-373.
15 MIHALCEAR, TARAUP. TextRank: bringing order into texts [EB/OL]. [2019-02-20].https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf.
16 BORDOLOIM, BISWASS K. Keyword extraction from micro-blogs using collective weight [J]. Social Network Analysis and Mining, 2018, 8(1): Article No.58.
17 叶雪梅,毛雪岷,夏锦春,等.文本分类TF-IDF算法的改进研究[J].计算机工程与应用,2019,55(2):104-109,161. YEX M, MAOX M, XIAJ C, et al. Improved approach to TF-IDF algorithm in text classification [J]. Computer Engineering and Applications, 2019, 55(2): 104-109, 161.
18 李新福,赵蕾蕾,何海斌,等.使用Logistic回归模型进行中文文本分类[J].计算机工程与应用,2009,45(14):152-154. LIX F, ZHAOL L, HEH B, et al. Using Logistic regression model for Chinese text categorization [J]. Computer Engineering and Applications, 2009, 45(14): 152-154.
19 陈海红.多核SVM文本分类研究[J].软件,2015,36(5):7-10. CHENH H. Research on the text classification based on multi-kernel support vector machine [J]. Computer Engineering & Software, 2015, 36(5): 7-10.
20 WANGP, XUB, XUJ, et al. Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification [J]. Neurocomputing, 2016, 174:806-814.
21 BANERJEEI, LINGY, CHENM C, et al. Comparative effectiveness of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures for radiology text report classification [J]. Artificial Intelligence in Medicine, 2019, 97: 79-88.
22 CHENGY, YEZ, WANGM, et al. Document classification based on convolutional neural network and hierarchical attention network [J]. Neural Network World, 2019, 29(2): 83-98.
23 卢宏涛,张秦川.深度卷积神经网络在计算机视觉中的应用研究综述[J].数据采集与处理,2016,31(1):1-17. LUH T, ZHANGQ C. Applications of deep convolutional neural network in computer vision [J]. Journal of Data Acquisition and Processing, 2016, 31(1): 1-17.
24 陈丹雯,张俊,韩兵,等.基于改进词袋模型的相似关键帧匹配方法[J].计算机工程与设计,2011,32(8):2752-2755,2844. CHEND W, ZHANGJ, HANB, et al. Near-duplicate keyframe matching based on advanced bag-of-words model [J]. Computer Engineering and Design, 2011, 32(8): 2752-2755, 2844.
25 武永亮,赵书良,李长镜,等.基于TF-IDF和余弦相似度的文本分类方法[J].中文信息学报,2017,31(5):138-145. WUY L, ZHAOS L, LIC J, et al. Text classification method based on TF-IDF and cosine similarity [J]. Journal of Chinese Information Processing, 2017, 31(5): 138-145.
26 李锋刚,梁钰,GAOX Z,等.基于LDA-wSVM模型的文本分类研究[J].计算机应用研究,2015,32(1):21-25. LIF G, LIANGY, GAOX Z, et al. Research on text categorization based on LDA-wSVM model [J]. Application Research of Computers, 2015, 32(1):21-25.

[1]	MA Jialiang, CHEN Bin, SUN Xiaofei. General object detection framework based on improved Faster R-CNN [J]. Journal of Computer Applications, 2021, 41(9): 2712-2719.
[2]	WANG Shuyan, HOU Zeyu, SUN Jiaze. Difference detection method of adversarial samples oriented to deep learning [J]. Journal of Computer Applications, 2021, 41(7): 1849-1856.
[3]	ZHANG Yang, JIANG Minghu. Authorship identification of text based on attention mechanism [J]. Journal of Computer Applications, 2021, 41(7): 1897-1901.
[4]	ZHANG Mingming, LU Qingning, LI Wenzhong, SONG Hu. Deep neural network compression algorithm based on combined dynamic pruning [J]. Journal of Computer Applications, 2021, 41(6): 1589-1596.
[5]	GUO Tianxiao, HU Qingrui, LI Jianwei, SHEN Yanfei. Fitness action recognition method based on human skeleton feature encoding [J]. Journal of Computer Applications, 2021, 41(5): 1458-1464.
[6]	ZHANG Wenye, SHANG Fangxin, GUO Hao. Mixed precision neural network quantization method based on Octave convolution [J]. Journal of Computer Applications, 2021, 41(5): 1299-1304.
[7]	YANG Li, WANG Shihui, ZHU Bo. Point-of-interest recommendation algorithm combing dynamic and static preferences [J]. Journal of Computer Applications, 2021, 41(2): 398-406.
[8]	WEN Chaodong, ZENG Cheng, REN Junwei, ZHANG Yan. Patent text classification based on ALBERT and bidirectional gated recurrent unit [J]. Journal of Computer Applications, 2021, 41(2): 407-412.
[9]	CHEN Yanru, ZHANG Tujingwa, DU Qian, RAN Maoliang, WANG Hongjun. Prediction of indoor thermal comfort level of high-speed railway station based on deep forest [J]. Journal of Computer Applications, 2021, 41(1): 258-264.
[10]	LIAO Shenglan, YIN Shi, CHEN Xiaoping, ZHANG Bo, OUYANG Yu, ZHANG Heng. Intent recognition dataset for dialogue systems in power business [J]. Journal of Computer Applications, 2020, 40(9): 2549-2554.
[11]	YIN Chunyong, HE Miao. Text classification based on improved capsule network [J]. Journal of Computer Applications, 2020, 40(9): 2525-2530.
[12]	WANG Minrui, GAO Shu, YUAN Ziyong, YUAN Lei. Sequence generation model with dynamic routing for multi-label text classification [J]. Journal of Computer Applications, 2020, 40(7): 1884-1890.
[13]	ZHANG Junsheng, XU Jingjing, YU Wei. No-reference image quality assessment method for facial beautification image [J]. Journal of Computer Applications, 2020, 40(4): 1184-1190.
[14]	WEI Wei, LI Xiaojuan. Patent quality evaluation using deep learning with similar papers as augmented dataset [J]. Journal of Computer Applications, 2020, 40(4): 966-971.
[15]	DENG Kai, HUNAG Jiajin, QIN Jin. Item-based unified recommendation model [J]. Journal of Computer Applications, 2020, 40(2): 530-534.

Automatic annotation of visual deep neural network

视觉类深度神经网络的自动标注

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics